From managed cloud deployments and self-managed clusters through real-time ingestion pipelines, OLAP schema design, and query performance engineering — our ClickHouse practice team helps enterprises unlock sub-second analytics over billions of rows of event, time-series, and operational data.
Fully managed ClickHouse as a service — zero-ops columnar analytics at petabyte scale, with automatic scaling, built-in replication, and enterprise SLAs on AWS, GCP, and Azure.
ClickHouse Cloud is the managed cloud offering of ClickHouse — delivering the full power of ClickHouse's columnar engine without the operational burden of running clusters. It auto-scales compute and storage independently, supports shared-nothing architecture, and provides SOC 2 Type II compliance, private networking via AWS PrivateLink and GCP Private Service Connect, and automated backups. Available in Development, Production, and Dedicated tiers, it suits teams from early-stage analytics to enterprise-scale data platforms processing trillions of events. Our practice guides organisations through ClickHouse Cloud onboarding, architecture design, cost optimisation, and integration with existing data stacks.
Selecting the right ClickHouse Cloud tier and configuration — evaluating Development vs. Production vs. Dedicated based on data volume, query concurrency, latency SLAs, and cost targets, with right-sizing recommendations for compute and storage.
Planning and executing migrations from on-premise ClickHouse, data warehouses (BigQuery, Snowflake, Redshift), or legacy RDBMS to ClickHouse Cloud — including schema conversion, data transfer strategy, and dual-write validation periods.
Configuring ClickHouse Cloud with AWS PrivateLink, GCP Private Service Connect, or Azure Private Link — ensuring all data traffic stays off the public internet, with IP allowlisting, SSO integration, and role-based access control.
Analysing ClickHouse Cloud usage patterns to reduce spend — implementing idle scaling policies, query result caching, tiered storage with S3, materialised view pre-aggregation, and right-sizing compute replicas for workload patterns.
Connecting ClickHouse Cloud to downstream analytics tools — Grafana, Superset, Metabase, Tableau, Power BI, and Looker — via the ClickHouse HTTP API, JDBC/ODBC drivers, or native connectors, with performance-tuned query patterns.
Configuring ClickHouse Cloud monitoring — using the built-in query log, system tables, and Prometheus-compatible metrics endpoint to build Grafana dashboards for query performance, memory usage, insert throughput, and error rate tracking.
Full control over your ClickHouse infrastructure — deploying the open-source or enterprise edition on Kubernetes, bare metal, or cloud VMs with cluster replication, sharding, and complete data sovereignty.
ClickHouse open-source is one of the fastest growing OLAP databases in the world, with a vibrant community and a rich ecosystem of integrations. Self-managed deployments give organisations complete control over hardware, networking, upgrade cadence, and data residency — critical for regulated industries, air-gapped environments, and extreme-scale deployments where cloud costs are prohibitive. ClickHouse supports horizontal sharding across multiple nodes, synchronous and asynchronous replication via ReplicatedMergeTree, and Keeper-based distributed coordination. Our practice covers the full lifecycle of self-managed ClickHouse clusters — from initial provisioning and topology design through security hardening, Kubernetes-native deployment, operational runbooks, and upgrade management.
Deploying ClickHouse on Kubernetes using the Altinity Kubernetes Operator or ClickHouse Operator — configuring StatefulSets, persistent volumes, resource limits, pod affinity rules, and Keeper/ZooKeeper for distributed coordination.
Designing ClickHouse cluster topologies — determining shard count, replica configuration, replication factors, and data distribution strategies using Distributed tables and consistent hashing to balance query parallelism with write amplification costs.
Hardening self-managed ClickHouse — configuring TLS for inter-node and client communication, LDAP/SSO integration, row-level and column-level access policies, network segmentation, and audit logging to system tables for compliance.
Implementing ClickHouse backup strategies using clickhouse-backup, S3-compatible object storage, and incremental snapshot policies — with documented restore procedures, RTO/RPO validation, and cross-region DR topology for business continuity.
Planning and executing ClickHouse version upgrades in production — managing rolling restarts across shards and replicas, validating compatibility of data formats and SQL dialect changes, and maintaining cluster availability throughout the upgrade window.
Building a full observability stack for self-managed ClickHouse — scraping system table metrics with Prometheus, building Grafana dashboards for merge queue depth, insert latency, replication lag, and query memory usage, with alert rules for operational thresholds.
Sub-second OLAP queries over billions of rows — enabling product analytics, operational intelligence, user-facing dashboards, and business reporting that responds at the speed of thought.
ClickHouse's columnar storage, vectorised query execution, and aggressive compression make it the database of choice for organisations that need interactive analytics over massive datasets — where traditional data warehouses are too slow or too expensive. Use cases span product analytics (DAU/MAU, funnel analysis, cohort retention), security and observability (log analytics, SIEM), financial analytics (time-series pricing, risk aggregation), and user-facing embedded analytics where queries run inside customer-facing applications. Our practice specialises in designing ClickHouse schemas and query patterns for specific analytical use cases, ensuring that the right table engines, indices, and pre-aggregation strategies are in place to deliver consistently fast query response times at any data scale.
Designing ClickHouse schemas optimised for analytical query patterns — selecting the right table engine (MergeTree, SummingMergeTree, AggregatingMergeTree, CollapsingMergeTree), partition keys, sorting keys, and primary index granularity for target workloads.
Designing and implementing ClickHouse materialised views that pre-aggregate metrics at insert time — enabling instant query response for common analytical patterns like hourly rollups, session aggregations, and per-user metric computations.
Architecting ClickHouse-backed embedded analytics for SaaS products — designing multi-tenant data isolation patterns, per-customer query scoping, and low-latency query APIs that serve interactive charts and dashboards directly to end users.
Building ClickHouse-based log analytics platforms as alternatives to Elasticsearch — designing schemas for structured and semi-structured log ingestion, implementing full-text search with bloom filter indices, and building Grafana dashboards for operational intelligence.
Designing ClickHouse schemas for high-cardinality time-series workloads — metrics, IoT sensor data, financial tick data, and event streams — using TTL-based data tiering, asof joins for nearest-timestamp lookups, and FilledNull for gap-filling queries.
Connecting ClickHouse to analytics frontends — Grafana (native ClickHouse plugin), Apache Superset, Metabase, Tableau, and Power BI — with query optimisation, connection pooling, and caching strategies for interactive dashboard workloads.
High-throughput data ingestion from any source — Kafka, S3, HTTP, CDC streams, and application databases — with exactly-once semantics, schema evolution, and production-grade reliability.
Getting data into ClickHouse reliably and efficiently is one of the most critical architectural decisions for any analytics platform. ClickHouse supports a wide range of native ingestion patterns: Kafka engine tables for direct Kafka consumption, S3 table functions for bulk loading from object storage, the HTTP interface for application-level inserts, and the ReplacingMergeTree and CollapsingMergeTree engines for CDC-style upserts. At high insert rates, batching strategy, async insert configuration, and buffer table design critically impact both throughput and query performance. Our practice helps organisations design and implement production-grade ingestion pipelines that balance insert throughput with query latency, handle schema evolution gracefully, and maintain data consistency under failure conditions.
Designing and implementing Kafka ingestion into ClickHouse — using the native Kafka engine table, Kafka Connect ClickHouse Sink connector, or Vector/Benthos pipelines — with consumer group management, offset tracking, exactly-once delivery, and schema registry integration.
Implementing Change Data Capture pipelines from operational databases (PostgreSQL, MySQL, MongoDB) into ClickHouse using Debezium, Airbyte, or ClickHouse's experimental MaterialisedMySQL/PostgreSQL engines — enabling near-real-time analytics over operational data.
Building bulk data loading pipelines from S3, GCS, or Azure Blob Storage into ClickHouse — using the S3 table function, S3Queue engine for continuous ingestion, and parallel loading strategies to maximise throughput for historical data backfills and batch ETL workflows.
Tuning ClickHouse async insert configuration for high-frequency application-level inserts — configuring async_insert, wait_for_async_insert, flush intervals, and buffer table patterns to maximise insert throughput while maintaining part count control and merge health.
Managing ClickHouse schema changes in production — implementing backward-compatible column additions, TTL policy modifications, partition key changes, and table engine migrations using detach/attach patterns and shadow copies to avoid downtime.
Designing end-to-end ETL architectures that land data in ClickHouse — evaluating and implementing dbt-ClickHouse, Apache Spark ClickHouse connector, Airbyte, Fivetran, and custom pipeline frameworks for batch and streaming ingestion patterns.
Squeezing every millisecond from ClickHouse — query profiling, index optimisation, merge health management, and hardware-aware tuning to sustain peak performance at production scale.
ClickHouse's performance is exceptional by default, but unlocking its full potential at scale requires deep understanding of its internals — how the MergeTree engine reads data, how the query optimiser uses primary and secondary indices, how memory allocation affects large aggregations, and how part merges interact with concurrent insert workloads. Performance degradation in ClickHouse is often caused by sub-optimal sorting key selection, excessive part counts from small inserts, unplanned full partition scans, or large JOIN operations that don't fit in memory. Our performance engineering practice conducts systematic performance assessments, query profiling sessions, and schema reviews — delivering actionable optimisation plans with measurable impact on query latency, resource efficiency, and infrastructure cost.
Profiling slow ClickHouse queries using EXPLAIN PLAN, EXPLAIN PIPELINE, query_log, and trace_log system tables — identifying full scans, excessive memory usage, inefficient JOIN strategies, and suboptimal aggregation plans, with targeted rewrite recommendations.
Reviewing and redesigning ClickHouse primary index (sorting key) and secondary index (skip index) configurations — selecting optimal column ordering and granularity for primary keys, and adding bloom filter, minmax, or set skip indices to eliminate unnecessary granule reads.
Diagnosing and resolving ClickHouse merge health issues — excessive part counts from high-frequency small inserts, merge queue backlogs, detached parts, and mutation backlogs — with configuration tuning for merge scheduler, max_parts_in_total, and insert batching.
Tuning ClickHouse memory settings for large analytical workloads — configuring max_memory_usage, max_bytes_before_external_group_by, join_algorithm selection, and mark cache sizing to prevent OOM errors while maximising query throughput per node.
Selecting optimal column-level compression codecs (LZ4, ZSTD, Delta, DoubleDelta, Gorilla) based on data type and cardinality — reducing storage footprint, improving cache hit rates, and accelerating query scan throughput through better compression ratios.
Conducting structured ClickHouse load tests using clickhouse-benchmark and custom query workloads — measuring throughput, latency percentiles, and resource utilisation under peak load conditions, and producing capacity plans for infrastructure scaling decisions.
Our engineers have hands-on experience running ClickHouse at scale — from schema design and ingestion pipeline architecture through performance tuning and production operations in high-throughput environments.
We start every engagement with a thorough requirements assessment — data volumes, query patterns, ingestion rates, and latency SLAs — designing a ClickHouse architecture that meets your needs today and scales with you tomorrow.
We connect ClickHouse to your existing data platform — Kafka, dbt, Airflow, Spark, S3, and your BI tools — so it becomes a seamlessly integrated component of your analytics infrastructure rather than an isolated silo.
Our performance engineering engagements deliver concrete, measurable results — query latency improvements, storage cost reductions, and insert throughput increases — backed by before/after benchmarks and documented optimisation changes.